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Petitjean S.,CNRS Orleans Fundamental Informatics Laboratory
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2014

XMG (eXtensible MetaGrammar) is a metagrammar compiler which has already been used for the design of large scale Tree Adjoining Grammars and Interaction Grammars. Due to the heterogeneity in the field of grammar development (different grammar formalisms, different languages, etc), a particularly interesting aspect to explore is modularity. In this paper, we discuss the different spots where this modularity can be considered in a grammar development, and its integration to XMG. © 2014 Springer-Verlag Berlin Heidelberg. Source


Lalande J.-F.,CNRS Orleans Fundamental Informatics Laboratory | Wendzel S.,Fraunhofer Institute for Communication, Information Processing and Ergonomics
Proceedings - 2013 International Conference on Availability, Reliability and Security, ARES 2013 | Year: 2013

Covert channels enable a policy-breaking communication not foreseen by a system's design. Recently, covert channels in Android were presented and it was shown that these channels can be used by malware to leak confidential information (e.g., contacts) between applications and to the Internet. Performance aspects as well as means to counter these covert channels were evaluated. In this paper, we present novel covert channel techniques linked to a minimized footprint to achieve a high covertness. Therefore, we developed a malware that slowly leaks collected private information and sends it synchronously based on four covert channel techniques. We show that some of our covert channels do not require any extra permission and escape well know detection techniques like TaintDroid. Experimental results confirm that the obtained throughput is correlated to the user interaction and show that these new covert channels have a low energy consumption - both aspects contribute to the stealthiness of the channels. Finally, we discuss concepts for novel means capable to counter our covert channels and we also discuss the adaption of network covert channel features to Android-based covert channels. © 2013 IEEE. Source


Havet F.,French Institute for Research in Computer Science and Automation | Klazar M.,Charles University | Kratochvil J.,Charles University | Kratsch D.,CNRS Theoretical and Applied Informatics | Liedloff M.,CNRS Orleans Fundamental Informatics Laboratory
Algorithmica (New York) | Year: 2011

The notion of distance constrained graph labelings, motivated by the Frequency Assignment Problem, reads as follows: A mapping from the vertex set of a graph G=(V,E) into an interval of integers {0,⋯,k} is an L(2,1)-labeling of G of span k if any two adjacent vertices are mapped onto integers that are at least 2 apart, and every two vertices with a common neighbor are mapped onto distinct integers. It is known that for any fixed k≥4, deciding the existence of such a labeling is an NP-complete problem. We present exact exponential time algorithms that are faster than the naive O *((k+1) n ) algorithm that would try all possible mappings. The improvement is best seen in the first NP-complete case of k=4, where the running time of our algorithm is O(1.3006 n ). Furthermore we show that dynamic programming can be used to establish an O(3.8730 n ) algorithm to compute an optimal L(2,1)-labeling. © 2009 Springer Science+Business Media, LLC. Source


Gaspers S.,Vienna University of Technology | Liedloff M.,CNRS Orleans Fundamental Informatics Laboratory
Discrete Mathematics and Theoretical Computer Science | Year: 2012

An independent dominating set D of a graph G = (V,E) is a subset of vertices such that every vertex in V n D has at least one neighbor in D and D is an independent set, i.e. no two vertices of D are adjacent in G. Finding a minimum independent dominating set in a graph is an NP-hard problem. Whereas it is hard to cope with this problem using parameterized and approximation algorithms, there is a simple exact O(1,4423 n)-time algorithm solving the problem by enumerating all maximal independent sets. In this paper we improve the latter result, providing the first non-trivial algorithm computing a minimum independent dominating set of a graph in time O(1,3569 n). Furthermore, we give a lower bound of σ(1,3247 n) on the worst-case running time of this algorithm, showing that the running time analysis is almost tight. © 2012 Discrete Mathematics and Theoretical Computer Science (DMTCS), Nancy, France. Source


Souto M.C.P.D.,CNRS Orleans Fundamental Informatics Laboratory | Jaskowiak P.A.,University of Sao Paulo | Costa I.G.,Federal University of Pernambuco | Costa I.G.,RWTH Aachen
BMC Bioinformatics | Year: 2015

Background: Several missing value imputation methods for gene expression data have been proposed in the literature. In the past few years, researchers have been putting a great deal of effort into presenting systematic evaluations of the different imputation algorithms. Initially, most algorithms were assessed with an emphasis on the accuracy of the imputation, using metrics such as the root mean squared error. However, it has become clear that the success of the estimation of the expression value should be evaluated in more practical terms as well. One can consider, for example, the ability of the method to preserve the significant genes in the dataset, or its discriminative/predictive power for classification/clustering purposes. Results and conclusions: We performed a broad analysis of the impact of five well-known missing value imputation methods on three clustering and four classification methods, in the context of 12 cancer gene expression datasets. We employed a statistical framework, for the first time in this field, to assess whether different imputation methods improve the performance of the clustering/classification methods. Our results suggest that the imputation methods evaluated have a minor impact on the classification and downstream clustering analyses. Simple methods such as replacing the missing values by mean or the median values performed as well as more complex strategies. The datasets analyzed in this study are available at http://costalab.org/Imputation/. © 2015 de Souto et al. Source

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